This project explores bipedal locomotion by training a humanoid agent to walk and maintain balance in a physics-based simulation. Built with Unity, ML-Agents, and Python, it uses Deep Reinforcement Learning (PPO) to develop a stable walking gait through a carefully designed reward-based system.